Aerial and other wide-area surveillance technologies now permit persistent, real-time observation of large scenes coupled with the ability to accurately determine the location of objects present in the scenes. Geolocation data, which can be images and other data about the location of objects in a scene, can be generated by geolocation sensors, such as GMTI or other radar, optical sensors, etc. In a given geolocation dataset, however, it maybe unclear whether a particular observed object is an object of interest, or whether it is some other object that merely has similar characteristics in the geolocation dataset. Radio frequency (RF) sensors and emitters are used to identify a particular object of interest within a geolocation dataset. Associating the identification of an object of interest using RF sensors with geolocation data that accurately describes the position of the object has been accomplished by comparing movement information generated by RF sensor data with positions of objects identified in the geolocation data. Position solutions or lines of bearing from RF sensors used in this process are generated using frequency difference of arrival (FDOA) or angle of arrival (AOA) methods that require long collection times or large baseline, multi-phase antennas. When the object of interest is moving, the position fix generated by these methods has significantly reduced accuracy or requires significant latency (e.g., several minutes) to acquire.